Increasing the storage capacity of a storage device using such a storage medium as an optical disk and magnetic disk is demanded, and for this, the recording density, particularly the track density of the storage medium is increasing remarkably. In such a storage device, the head for reading and/or writing the storage medium must follow up the storage medium at high precision. In an optical disk, for example, focus and track control of the optical beam to the optical disk are required. And to increase the accuracy of the follow-up control system, a disturbance near one or a plurality of specific frequencies deteriorates the follow-up accuracy conspicuously, so a control system for suppressing this effectively is demanded.
For example, in the track follow-up control of an optical disk, the disturbance of a specific frequency may have a dominant negative effect on the track follow-up accuracy. Generally there is a disturbance synchronizing with the rotation of the optical disk, and a disturbance not synchronizing with the rotation. The most conspicuous disturbance synchronizing with rotation is a disturbance due to rotation frequency and an integral multiple of the rotation frequency caused by eccentricity.
It has been shown that such a disturbance can be efficiently compensated by a learning control system synchronizing rotation (e.g. Japanese Patent Application Laid-Open No. 2000-339729, U.S. Pat. No. 4,616,276, and the publication “Modern Control Series 4, ‘Motion Control’, (by Dote and Harashima), published by Corona Ltd.”).
In other words, in these learning controls, a low frequency disturbance can be easily compensated when the disturbance frequency is low frequency (e.g. rotation frequency).
For example, the disturbance removal method by learning disclosed in U.S. Pat. No. 4,616,276 and the publication “Modern Control Series 4, ‘Motion Control’ (by Dote and Harashima), published by Corona Ltd.” will be described with reference to FIG. 27 and FIG. 28.
This disturbance removal method is to express the model of a signal (unknown) for compensating frequency disturbance to be a problem by a composite representation of sin and cos, and to adaptively identify the amplitude gain of the respective component of the signal sequentially, thereby sequentially identifying the amplitude and phase of the target frequency disturbance and compensating the target frequency disturbance by feed-forwarding the identification result.
This will be described in detail with reference to FIGS. 27(A) and 27(B). When the target is a specific frequency disturbance, the model representing the control signal for compensating disturbance which has the frequency fd [Hz] (hereafter called disturbance compensation signal) is represented with the amplitude A and the phase φ as the following formula (1).Dfd=A×sin(2×π×fd×t+φ)  (1)Also the formula (1) is expanded to the formula (2) by a composite formula of trigonometric functions.
                                                                                                              Dfd                    =                                        ⁢                                          A                      ×                                              sin                        ⁡                                                  (                                                                                    2                              ×                              π                              ×                              fd                              ×                              t                                                        +                            ϕ                                                    )                                                                                                                                                                                                                                                    ⁢                                          =                                            ⁢                                                                        a                          ×                                                      sin                            ⁡                                                          (                                                              2                                ×                                π                                ×                                fd                                ×                                t                                                            )                                                                                                      +                                                                                                                                                                                                 ⁢                                          b                      ×                                              cos                        ⁡                                                  (                                                      2                            ×                            π                            ×                            fd                            ×                            t                                                    )                                                                                                                                                                                                                              Here                  ⁢                                                                          ⁢                  A                                =                                  (                                                                                    a                        ⋀                                            ⁢                      2                                        +                                                                  b                        ⋀                                            ⁢                      2                                                        )                                            ,                              ϕ                =                                                      tan                                          -                      1                                                        ⁢                                                                          ⁢                                      (                                          b                      /                      a                                        )                                                                                                          (        2        )            
In this way, the control signal is represented by the weighted sum of the sine and cosine functions removing the phase φ. In other words, an arbitrary disturbance compensation signal having the frequency fd [Hz] (that is a disturbance compensation signal having the frequency fd [Hz] which has an arbitrary amplitude A and an arbitrary initial phase φ) can be represented by determining the amplitudes a and b for the sine and cosine functions in formula (2) respectively.
Here the values a and b are obtained on line by a learning rule (also called an adaptive-rule). FIG. 27(A) is a diagram depicting the sine wave signal learning section 100 which learns the amplitude and the phase of an arbitrary sine wave signal having the frequency fd which was input to Xref, and outputs the signal Y where the learned amplitude and phase are copied.
The error e indicates the error between the input signal Xref and the signal Y which was copied by the learning section 100. The sine wave signal learning section 100 updates the values a and b sequentially by inputting the error e, and outputs the latest sine wave signal generation result Y. The values a and b are sequentially updated according to the following learning rule by the formula (3).a(t)=k×sin(2×π×fd×t)×e(t)b(t)=k×cos(2×π×fd×t)×e(t)  (3)And the latest sine wave signal generation result Y is sequentially output by the following formula (4).Y(t)=a(t)×sin(2×π×fd×t)+b(t)×cos(2×π×fd×t)  (4)
As learning progresses and Y (t) becomes equal to Xref (t), the error e (t) becomes “0”, the rate of change of the values a and b represented by the formula (3) become zero respectively, and learning converges. The sine wave signal learning section 100 described above has the function to extract sine wave signals, which have a preset specific frequency, out of the input signal, and the functions to integrate and output the sine wave signals.
Therefore in the configuration in FIG. 27(A), specific frequency components which are preset are extracted from the error signals, are integrated and output, and when the error becomes “0”, integration stops.
When the present principle is applied to the follow-up control system of the storage device, the sine wave signal learning section 100 is integrated into a normal control system 101, as shown in FIG. 27(B). The sine wave signal learning section 100 extracts a specific frequency component which is preset in the direction to make the error signal “0” concerning the follow-up error signal PES, and integrates and outputs it. Therefore if the sine wave signal learning section 100 is integrated into the control system in this way, the compensation signal is finally output for the specific frequency component, which is preset, after learning. In other words, the influence of the disturbance of that frequency can be removed (suppressed).
In such a prior art, the disturbance of eccentric frequency synchronizing disk rotation, which is a conspicuous frequency disturbance in the tracking control system and the focus control system of a storage disk, such as an optical disk, can be effectively suppressed.
In the case of the learning method according to the above mentioned Japanese Patent Application Laid-Open No. 2000-339729, the rotation cycle is divided into N, and N number of values corresponding to each divided area become the learning target (e.g. divided into 32, and 32 values are learned), so time for converging learning is required after the disk rotates once so that learning is performed for each value, and this learning takes time, however the removal of a disturbance can be executed by a simpler operation. In other words, if a digital processor is used, the remove of a disturbance can be implemented by fewer operation steps.
On the other hand, in the case of the learning control using sin and cos, as in U.S. Pat. No. 4,616,276, and the publication “Modern Control Series 4, ‘Motion Control’ (by Dote and Harashima), published by Corona Ltd.”, the number of parameters to be learned is 2 per frequency (weight with respect to the sine and cosine functions), and as the learning rule in formula (4) shows, the update operation is constantly performed. While in the learning control system of the above mentioned Japanese Patent Application Laid-Open No. 2000-339729, the update operation is performed for N number of parameters only in the corresponding time block of each rotation. Therefore the former learning control using sin and cos allows faster learning than the latter learning control. For example, learning can be converged in a very short time within 1 cycle of a disk rotation. However the computing processing is complicated, so this learning control is not suitable for -processors which processing speed is slow.
Conventional learning control, as seen in Japanese Patent Application Laid-Open No. 2000-339729, on the other hand, can suitably support the suppression of frequency component synchronizing rotation which has a certain frequency or less, as shown in FIG. 28, but the learning takes time to suppress the frequency disturbance due to a high frequency which tends to fluctuate (particularly a high frequency disturbance not synchronizing rotation). In other words, in the case of the learning method of Japanese Patent Application Laid-Open-No. 2000-339729, learning takes time, particularly the learning of a high frequency disturbance which tends to fluctuate after seeking, and in the case of the learning method of U.S. Pat. No. 4,616,276 etc., high-speed learning is possible but learning does not converge so easily due to the phase lag of the control system, and it is difficult to suppress high frequency disturbance effectively.
Because of this, for disturbance not synchronizing rotation, a method for suppressing the disturbance by improving the disturbance suppression performance of the feedback control system is used. For frequency disturbance due to a frequency which is somewhat lower than the control band, a sufficient suppression performance is frequently obtained by increasing the degree of the integration compensation of the feedback controller, for example. However, if the disturbance of a specific frequency, which is close to the control band or exceeds the control band, exists conspicuously, it is extremely difficult to suppress the disturbance by a linear feedback controller.
When a desired track follow-up accuracy cannot be achieved due to such a disturbance, all that is possible is to generally investigate the generation source of the disturbance, and to decrease the amplitude of the disturbance itself by improving the design of the mechanism, for example.